Machine Learning Within Studies of Early-Life Environmental Exposures and Child Health: Review of the Current Literature
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EARLY LIFE ENVIRONMENTAL HEALTH (H VOLK & J BUCKLEY, SECTION EDITORS)
Machine Learning Within Studies of Early-Life Environmental Exposures and Child Health: Review of the Current Literature and Discussion of Next Steps Sabine Oskar 1 & Jeanette A. Stingone 1
# Springer Nature Switzerland AG 2020
Abstract Purpose of Review The goal of this article is to review the use of machine learning (ML) within studies of environmental exposures and children’s health, identify common themes across studies, and provide recommendations to advance their use in research and practice. Recent Findings We identified 42 articles reporting upon the use of ML within studies of environmental exposures and children’s health between 2017 and 2019. The common themes among the articles were analysis of mixture data, exposure prediction, disease prediction and forecasting, analysis of complex data, and causal inference. Summary With the increasing complexity of environmental health data, we anticipate greater use of ML to address the challenges that cannot be handled by traditional analytics. In order for these methods to beneficially impact public health, the ML techniques we use need to be appropriate for our study questions, rigorously evaluated and reported in a way that can be critically assessed by the scientific community. Keywords Machine learning . Child health . Environmental mixtures . Environmental health . Data-science prenatal environment
Introduction The last decade has marked an increase in the interest of using machine learning (ML) techniques for health research. A simple PubMed search of “machine learning” AND “health” shows that the number of published articles has grown from 61 in 2010 to 2712 in 2019. ML, a branch of artificial intelligence, has been defined as “a set of methods that can automatically detect patterns in data, and then use the uncovered patterns to predict future data, or to perform decision-making under uncertainty” [1]. While many ML methods have been around for decades, the more recent availability of computational resources and big health data has ignited a renewed enthusiasm for their use in a range of substantive fields within medicine and public health. There have been a growing This article is part of the Topical Collection on Early Life Environmental * Jeanette A. Stingone [email protected] 1
Department of Epidemiology, Columbia University Mailman School of Public Health, 722 West 168th St, Room 1608, New York, NY 10032, USA
number of commentaries and reviews on the use of ML within the health sciences [2••, 3, 4]. This includes areas within environmental health [5]. ML methods have typically been used for research questions related to prediction and classification [6]. In contrast, environmental health has more commonly focused on causal or explanatory modeling, addressing research questions that seek to estimate the magnitude and precision of health effects related to environmental contaminants. Emerging technologies, such as electronic health records, -omics platforms, social media
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